Reference-Free Evaluation of Reasoning in Open-Ended Question Answering

2026-07-21

AI Summary

This work addresses the challenge of verifying fluent yet potentially unreliable multi-step reasoning generated by AI in high-stakes domains. The authors propose the first reference-free reasoning evaluation framework, which decomposes reasoning trajectories into segments, annotates local premise-conclusion relationships using natural language inference (NLI), and constructs a hypergraph to represent their logical structure. By applying deterministic backward AND-OR search over this hypergraph, the method assigns audit labels to each segment, quantifying the strength of its internal logical support. Innovatively integrating NLI with hypergraph-based representation, the approach emphasizes the compositional nature of inferential relations within reasoning chains, eschewing reliance on final answers or LLM-based judges. Evaluated on mathematical (Hard2Verify) and clinical reasoning (UroReason) tasks, the framework significantly outperforms LLM judges, particularly excelling at identifying logically weak yet linguistically fluent reasoning segments in clinical contexts.

Abstract

AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer. We propose a reasoning-based, reference-free framework for auditing LLM-generated outputs. The method decomposes a generated reasoning trace into segments, labels local premise-target relations using Natural Language Inference (NLI), and organizes these relations into a hypergraph. A deterministic backward AND-OR search then assigns segment-level audit labels that indicate how each segment is grounded within the generated response. We evaluate the framework in two settings: deductive mathematical reasoning with Hard2Verify, and open-ended medical reasoning with UroReason, a new physician-annotated benchmark of LLM reasoning traces from real clinical cases. Across these settings, our NLI-hypergraph audit provides a more reliable reference-free evaluation signal than direct LLM-as-judge baselines. In the clinical setting, state-of-the-art LLM judges often fail to identify problematic reasoning segments, over-accepting fluent but weakly grounded responses. Our results show that QA evaluation should account for how inferential relations compose across a reasoning trace, rather than relying only on final answers or LLMs as verifiers. UroReason will be made available through an API, and our code will be released as open source.

Problem

• reference-free evaluation
• reasoning trace
• open-ended question answering
• LLM auditing
• inference validation

Innovation

• reference-free evaluation
• reasoning trace audit
• NLI-hypergraph
• AND-OR search
• open-ended QA